Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
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BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises.
Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.
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Alkemi
Alkemi's premier offering, DataLab, serves as a secure AI-driven workspace that facilitates direct connections to your organization's regulated data from platforms such as Snowflake, BigQuery, Databricks, or even straightforward CSV uploads, allowing users to pose questions in everyday language and receive immediate, clear answers, visualizations, and suggestions without needing SQL expertise or analyst assistance. Within a private and secure environment, DataLab systematically indexes and examines your data, ensuring that every insight is both traceable and verifiable, maintaining the integrity of your data and protecting intellectual property and governance. By merging the complexities of data storage with accessible decision-making, it enhances the clarity of business intelligence through conversational AI, effectively minimizing BI backlogs and accelerating decision-making across various sectors, including marketing, finance, product, sales, and operations. Furthermore, DataLab empowers data providers to transform their datasets into engaging, AI-ready experiences that can be securely navigated by buyers, facilitating faster data discovery while safeguarding the integrity of the underlying raw data. This innovative approach not only streamlines workflows but also fosters a culture of data-driven decision-making within organizations.
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PHEMI Health DataLab
Unlike most data management systems, PHEMI Health DataLab is built with Privacy-by-Design principles, not as an add-on. This means privacy and data governance are built-in from the ground up, providing you with distinct advantages:
Lets analysts work with data without breaching privacy guidelines
Includes a comprehensive, extensible library of de-identification algorithms to hide, mask, truncate, group, and anonymize data.
Creates dataset-specific or system-wide pseudonyms enabling linking and sharing of data without risking data leakage.
Collects audit logs concerning not only what changes were made to the PHEMI system, but also data access patterns.
Automatically generates human and machine-readable de- identification reports to meet your enterprise governance risk and compliance guidelines.
Rather than a policy per data access point, PHEMI gives you the advantage of one central policy for all access patterns, whether Spark, ODBC, REST, export, and more
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